Multispectral infrared imaging gas detection method based on microlens and optical filter array

Abstract

The invention discloses a multispectral infrared imaging gas detection method based on a micro lens and an optical filter array, is suitable for multispectral infrared gas detection of industrial hazardous gas leakage, and belongs to the technical field of infrared imaging and gas detection. A multispectral infrared imaging system is formed based on a single uncooled infrared focal plane detector, a micro-lens array and a partition optical filter array, and a plurality of spectral intervals respectively correspond to optical absorption peaks of specific gas; by utilizing the capability of unifying the response characteristics of a single detector, the spectrum normalized absorptivity curve of each wave band can be conveniently calculated and compared with a special gas spectrum normalized absorptivity curve database, and the type of the current gas can be judged. When the multispectral infrared imaging gas detection system is applied to the field of gas detection, the problem that the existing infrared imaging system cannot effectively distinguish gas types can be solved, the gas detection capability of the infrared imaging system is improved, the proposed method can automatically monitor gas leakage, and the automatic detection level of the multispectral infrared imaging gas detection system is improved.

Description

Technical Field

[0001] The present invention relates to a multi-spectral infrared imaging gas detection method based on microlenses and filter arrays, and in particular to a multi-spectral infrared imaging system technology, which is suitable for multi-spectral infrared gas detection of industrial hazardous gas leakage and belongs to the field of infrared imaging and gas detection technology. Background Art

[0002] Industrial hazardous gas leak detection technology is a technology widely used in the industrial field. It is usually used to detect leaked gas based on the physical or chemical properties of the leaked gas. Traditional gas detection sensors use contact measurement methods, but they need to be deployed in large numbers at various potential leak points, and the leak points cannot be located intuitively. The infrared imaging gas detection technology developed in recent years is based on the infrared spectral absorption characteristics of leaked gas. Detectors are designed for detection in specific infrared bands absorbed by the gas spectrum. It can intuitively display traces of leaked gas, making it easier to determine the scale of the leak and locate the leak point, and the method of use is more intuitive.

[0003] Currently, the main application is the gas detection equipment based on the single-band infrared imaging system. Although it can observe the tail of dangerous gas leakage that the visible light band imaging system cannot capture, there is usually more than one type of leaking gas in actual industrial scenarios, and it includes safe gases such as water vapor and carbon dioxide, which will interfere with the infrared image and make the system unable to distinguish between dangerous gases and safe gases, posing a challenge to the existing single-spectrum infrared imaging gas detection technology.

[0004] Multi-spectral infrared imaging technology usually has several to dozens of infrared imaging spectra, which can be divided into several independent bands within the 3-14μm band for imaging, and targeted imaging is performed for the characteristics of a certain gas with multiple infrared absorption peaks within the 3-14μm band. For example, methane gas usually has absorption in the 3.2-3.4μm and 6-8μm bands, so by configuring a multi-spectral infrared imaging system to divide the 3-14μm band into multiple bands with equal width, the current gas can be identified as methane through the gas cloud traces of methane gas in the 3-4μm and 6-7μm bands.

[0005] Hazardous gas detection technology based on multispectral infrared imaging technology allows equipment users to effectively distinguish between various types of leaked gases and eliminate interference from common gases such as water vapor and carbon dioxide, effectively improving the detection capabilities of current infrared imaging gas detection technology. In order to improve the level of automated monitoring of hazardous gases, it is also necessary to design a solution for automated identification of leaked gas types based on a multispectral infrared imaging system and by utilizing the information differences in multispectral infrared images, so as to facilitate the implementation of automatic alarm technology for equipment in actual use scenarios and improve the automation level of hazardous gas monitoring systems. Summary of the invention

[0006] The object of the present invention is to provide a multi-spectral infrared imaging gas detection method based on microlenses and filter arrays, which is used for gas cloud target detection of dangerous gas leakage.

[0007] The objective of the present invention is achieved through the following technical solutions:

[0008] The multi-spectral infrared imaging gas detection method based on microlens and filter array disclosed in the present invention comprises the following steps:

[0009] S1. Build a multi-spectral infrared imaging system based on a microlens array and a partition filter, wherein the multi-spectral infrared imaging system includes an uncooled infrared focal plane detector, a microlens array corresponding to the focal plane array, and a partition filter; the microlens array corresponds to multiple partition filters one by one; the scene light beam passes through the multi-partition filter to form multiple light beams with different spectra; each light beam is focused to the corresponding area of ​​the uncooled infrared focal plane detector after passing through the corresponding microlens, and is collected by the uncooled infrared focal plane detector, that is, each spectrum is imaged on the same detector, and when the spectral absorption characteristics of the gas cloud target in each band are analyzed in the subsequent steps, the imaging characteristics of the multi-spectral image are consistent, and the spectral characteristics of the gas cloud target can be accurately analyzed according to the image grayscale; each image output by the uncooled infrared focal plane detector after collection is composed of multiple sub-images;

[0010] S2. Use the multispectral infrared imaging system built in S1 to collect images of hazardous gas leakage; collect an image of the hazardous area, and perform multispectral image registration on multiple sub-images in the image according to the characteristics of the scene; then perform image preprocessing on the multiple sub-images to eliminate stripe noise and Gaussian noise in the image; then crop the multiple sub-images to a uniform size and then splice them to obtain a complete image Y containing each channel.

[0011] S3, use the GMM detection method to statistically model the complete image Y obtained in S2, obtain the static background image X, and then use the current frame Y of image Y t The current frame X with image X t Make a difference to obtain the current frame G of the grayscale image of the gas cloud target in each channel t ; Based on the current frame G of the gas cloud target grayscale image t , select the gas cloud area, and for each pixel G in the gas cloud area t (i, j) to find the average value and obtain the average grayscale P of the gas cloud absorption in the current spectral channel k ; Then the average grayscale of gas cloud absorption in each spectral channel is normalized to obtain the spectral absorption rate curve Q of each band.

[0012] S4. Find the possible leaked gas A from the public or self-collected gas standard spectrum database, and then obtain the high-resolution spectrum curve T of gas A.o ; According to the spectral range corresponding to each sub-image in S2, the curve T o The spectral absorption rate of the corresponding spectral range is averaged, that is, the average gas absorption rate of the corresponding band of each sub-image is obtained; the average gas absorption rate corresponding to each sub-image is normalized to obtain the normalized curve T 1 ;

[0013] S5. Obtain N normalized curves T using the method described in S4. i (i=1,2···N), that is, N normalized curves T corresponding to N gases that may leak in S2 are obtained i (i=1,2···N).

[0014] S6, the curve T obtained by S5 i The leaked gas types are matched one by one with the curve Q obtained by S3, that is, multi-spectral infrared imaging gas detection is realized based on the microlens array and partition filter.

[0015] The specific method is to transform the curve T i Compare with curve Q and take curve T with the highest correlation coefficient i , curve T i The corresponding gas is the leaked gas, that is, multi-spectral infrared imaging gas detection is achieved based on the microlens array and partition filter.

[0016] Beneficial effects:

[0017] 1. The multi-spectral infrared imaging gas detection method based on microlens array and partition filter disclosed in the present invention is based on a single uncooled infrared focal plane detector and a microlens array and a partition filter array to form a multi-spectral infrared imaging system. The ability to unify the response characteristics of a single detector is used to facilitate the calculation of the normalized absorption rate curve of each band spectrum and to compare it with a dedicated gas spectrum normalized absorption rate curve database to determine the type of current gas. When applied to the field of gas detection, it can solve the problem that the existing infrared imaging system cannot effectively distinguish the type of gas, improve the gas detection capability of the infrared imaging system, and the proposed method can perform automatic monitoring of gas leaks, improving the automatic detection level of the multi-spectral infrared imaging gas detection system.

[0018] 2. The multi-spectral infrared imaging gas detection method based on microlens array and partition filter disclosed in the present invention designs multi-spectral bands for common gases in the current petrochemical field, extracts the grayscale information of gas cloud targets based on the spectral characteristics of leaked gas in infrared multi-spectral channels and the foreground detection method, constructs the spectral absorption rate curve of gas cloud targets in each channel, and calculates the curve T i Pearson correlation coefficient with curve Q, take the curve T with the highest correlation coefficient i, curve T i The corresponding gas is the leaking gas, thereby improving the accuracy and efficiency of identifying common leaking gas types. The present invention can effectively distinguish gas types during actual detection, eliminate the interference of safe gases such as carbon dioxide and water vapor, determine the gas type of the current gas cloud target, and improve the detection capability and practicality of the infrared imaging hazardous gas detection system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of a multi-spectral infrared imaging gas detection method based on a microlens array and a partition filter according to an embodiment of the present invention;

[0020] Figure 2 This is a schematic diagram of imaging of a multi-spectral infrared imaging gas detection system based on a microlens array and a partition filter for gas leakage detection in industrial scenarios proposed in an embodiment of the present invention;

[0021] Figure 3 This is a multispectral image of difluoroethane leakage collected by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] In order to better illustrate the purpose and advantages of the present invention, the invention is further described below with reference to the accompanying drawings and examples.

[0023] Embodiment 1:

[0024] This embodiment discloses a multi-spectral infrared imaging gas detection method based on a microlens array and a partitioned filter, which is intended to detect leaks in a production pipeline of a chemical plant. The gases that may be released from the pipelines of the chemical plant with leakage risks include: methane, ethylene, hydrogen sulfide, hydrogen chloride, ammonia, etc. Assuming that an ethylene gas leak occurs, the detection goal is to automatically identify the gas plume in the multi-spectral image as ethylene based on the proposed method. The implementation steps are as follows: Figure 1 shown.

[0025] like Figure 1 As shown, the multi-spectral infrared imaging gas detection method based on microlens and filter array disclosed in this embodiment has the following specific implementation steps:

[0026] S1. The multi-spectral infrared imaging system based on microlens array and partition filter mainly includes: uncooled infrared focal plane detector, microlens array corresponding to the focal plane array and partition filter, and the imaging optical path is as follows: Figure 2As shown. The microlens array corresponds to multiple partition filters one by one; the scene light beam passes through the multi-partition filters to form multiple light beams with different spectra; each light beam passes through the corresponding microlens and is focused to the corresponding area of ​​the uncooled infrared focal plane detector, and is collected by the uncooled infrared focal plane detector, that is, each spectrum is imaged on the same detector. When analyzing the spectral absorption characteristics of gas cloud targets in each band in the subsequent steps, the imaging characteristics of the multi-spectral image are consistent, and the spectral characteristics of the gas cloud target can be accurately analyzed according to the image grayscale; each image output by the uncooled infrared focal plane detector after collection is composed of multiple sub-images.

[0027] The microlens array includes 3×2 lens groups, where a single lens group consists of two infrared lenses. The spectral bands of the 3×2 filter groups are 3-5μm (channel 1), 6-8μm (channel 2), 7-9μm (channel 3), 9-11μm (channel 4), 10-12μm (channel 5), and 12-14μm (channel 6).

[0028] S2, using the multi-spectral infrared imaging system obtained in S1 to collect images of dangerous gas leakage; collect an image of the dangerous area, such as Figure 3 As shown. According to the features in the scene, the six sub-images in the image are multispectral image registered; then the six sub-images are image preprocessed to eliminate the stripe noise and Gaussian noise in the image; then the six sub-images are cropped to a uniform size and spliced ​​to obtain a complete image Y containing each channel, as shown Figure 3 shown.

[0029] S3, use the GMM detection method to statistically model the complete image Y obtained in S2, obtain the static background image X, and then use the current frame Y of image Y t The current frame X with image X t Make a difference to obtain the current frame G of the grayscale image of the gas cloud target in each channel t ; Based on the current frame G of the gas cloud target grayscale image t , select the gas cloud area, and for each pixel G in the gas cloud area t (i, j) to find the average value and obtain the average grayscale P of the gas cloud absorption in the current spectral channel k ; Then the average grayscale of gas cloud absorption in each spectral channel is normalized to obtain the spectral absorption rate curve Q of each band.

[0030] S4, obtain five curves corresponding to the five gases that may be leaking in S2; find five gases, methane, ethylene, hydrogen sulfide, hydrogen chloride, and ammonia, from the public gas standard spectrum database of the National Institute of Standards and Technology (NIST), and then obtain the high-resolution spectrum curve T of each gaso ; According to the spectral range corresponding to each sub-image in S2, the curve T o The spectral absorption rate of the corresponding spectral range is averaged, that is, the average gas absorption rate of the corresponding band of each sub-image is obtained; the average gas absorption rate corresponding to each sub-image is normalized to obtain the normalized curve T 1 .

[0031] S5. Use the method described in S4 to obtain five normalized curves T i (i=1,2···5);

[0032] S6, the five curves obtained by S5 T i Match the curve Q obtained by S3 to find the type of leaked gas; the specific method is to match the curve T i Calculate the Pearson correlation coefficient with curve Q and take the curve T with the highest correlation coefficient i , curve T i The corresponding gas is the leaked gas.

[0033] The specific description above further illustrates the purpose, technical solutions and beneficial effects of the invention in detail. It should be understood that the above is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-spectral infrared imaging gas detection method based on microlens and filter array, characterized in that: The following steps are included: S1. Build a multi-spectral infrared imaging system based on a microlens array and a partition filter, wherein the multi-spectral infrared imaging system includes an uncooled infrared focal plane detector, a microlens array corresponding to the focal plane array, and a partition filter; the microlens array corresponds to multiple partition filters one by one; the scene light beam passes through the multi-partition filter to form multiple light beams with different spectra; each light beam is focused to the corresponding area of ​​the uncooled infrared focal plane detector after passing through the corresponding microlens, and is collected by the uncooled infrared focal plane detector, that is, each spectrum is imaged on the same detector, and when the spectral absorption characteristics of the gas cloud target in each band are analyzed in the subsequent steps, the imaging characteristics of the multi-spectral image are consistent, and the spectral characteristics of the gas cloud target are accurately analyzed according to the image grayscale; each image output by the uncooled infrared focal plane detector after collection is composed of multiple sub-images; S2, using the multispectral infrared imaging system built in S1 to collect images of dangerous gas leakage; collect an image of the dangerous area, and perform multispectral image registration on multiple sub-images in the image according to the characteristics of the scene; then perform image preprocessing on the multiple sub-images to eliminate stripe noise and Gaussian noise in the image; then crop the multiple sub-images to a uniform size and then splice them to obtain a complete image Y containing each channel; S3, use the GMM detection method to statistically model the complete image Y obtained in S2, obtain the static background image X, and then use the current frame Y of image Y t The current frame X with image X t Make a difference to obtain the current frame G of the grayscale image of the gas cloud target in each channel t ; Based on the current frame G of the grayscale image of the gas cloud target t , select the gas cloud area, and for each pixel G in the gas cloud area t (i, j) to find the average value and obtain the average grayscale P of the gas cloud absorption in the current spectral channel k ; Then grayscale normalize the average grayscale of gas cloud absorption in each spectral channel to obtain the spectral absorption rate curve Q of each band; S4. Find the possible leaked gas A from the public or self-collected gas standard spectrum database, and then obtain the high-resolution spectrum curve T of gas A. o ; According to the spectral range corresponding to each sub-image in S2, the curve T o The spectral absorption rate of the corresponding spectral range is averaged, that is, the average gas absorption rate of the corresponding band of each sub-image is obtained; the average gas absorption rate corresponding to each sub-image is normalized to obtain the normalized curve T1; S5. Obtain N normalized curves T using the method described in S4. i (i=1,2···N), that is, N normalized curves T corresponding to N gases that may leak in S2 are obtained i (i=1,2···N); S6, the curve T obtained by S5 i The leaked gas types are matched one by one with the curve Q obtained by S3, that is, multi-spectral infrared imaging gas detection is realized based on the microlens array and partition filter.

2. The multi-spectral infrared imaging gas detection method based on microlens and filter array as claimed in claim 1, characterized in that: In S6, the curve T i Compare with curve Q and take curve T with the highest correlation coefficient i , curve T i The corresponding gas is the leaked gas, that is, multi-spectral infrared imaging gas detection is achieved based on the microlens array and partition filter.

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